This dataset contains 10,000 fluid flow and heat transfer simulations in U-bend shapes. Each of them is described by 28 design parameters, which are processed with the help of Computational Fluid Dynamics methods. The dataset provides a comprehensive benchmark for investigating various problems and methods from the field of design optimization. For these investigations supervised, semi-supervised and unsupervised deep learning approaches can be employed. One unique feature of this dataset is that each shape can be represented by three distinct data types including design parameter and objective combinations, five different resolutions of 2D images from the geometry and the solution variables of the numerical simulation, as well as a representation using the cell values of the numerical mesh. This third representation enables considering the specific data structure of numerical simulations for deep learning approaches. The source code and the container used to generate the data are published as part of this work.

翻译：本数据集包含10,000个U型弯道形状下的流体流动与传热模拟案例。每个案例由28个设计参数描述，并通过计算流体动力学方法进行处理。该数据集为研究设计优化领域的各类问题与方法提供了全面的基准测试平台。针对这些研究，可采用监督、半监督及无监督深度学习方法。该数据集的独特之处在于，每种形状可由三种不同数据类型表示：设计参数与目标变量组合、几何与数值模拟解变量五种不同分辨率的二维图像，以及基于数值网格单元值的表示方式。第三种表示方法使深度学习模型能够考虑数值模拟特有的数据结构。生成数据所用的源代码及容器已作为本研究的一部分公开发布。